Toward high entropy material discovery for energy applications using computational and machine learning methods
Bibliographic record
Abstract
Machine learning and computational methods can accelerate materials discovery by accurately predicting material properties at low cost. Nevertheless, input data to algorithms and structure model parameters remains a key obstacle. The limitations of conventional battery materials could be overcome by high-entropy materials, a unique class of special valuable materials. The knowledge of designing the crystal structure of high-entropy materials is advancing the design and fabrication of new materials for batteries and supercapacitors, even before chemical synthesis, through the use of learning algorithms and quantum computing. In this review, we first focus on quantum computing and the structure of high-entropy materials, especially high-entropy MXenes. We then discuss how to encode and decode the crystal structure of materials, which is a key factor in creating a database for high-entropy materials. We also discuss how to utilize deep learning algorithms for material discovery prior to synthesis, as well as how to employ these algorithms to identify high-entropy materials suitable for batteries and supercapacitors. Finally, we discuss the potential of new quantum computing and artificial intelligence approaches for determining the structure of high-entropy materials in the energy fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".